Systems and methods for auditing assets

ABSTRACT

In one embodiment, a method includes receiving first Light Detection and Ranging (LiDAR) data associated with a railroad environment, extracting an asset from the first LiDAR data associated with the railroad environment, and superimposing the asset into a spatial model. The method also includes receiving a field indication associated with a modification to the railroad environment and modifying the spatial model in response to receiving the field indication associated with the modification to the railroad environment. The method further includes receiving second LiDAR data associated with the railroad environment and comparing the second LiDAR data to the modified spatial model.

TECHNICAL FIELD

This disclosure generally relates to auditing assets, and morespecifically to systems and methods for auditing assets.

BACKGROUND

Positive train control (PTC) is a communications-based train controlsystem used to prevent accidents involving trains. PTC improves thesafety of railway traffic by auditing railroad track data. However, thetrack data used by the PTC system may misrepresent the actual locationof assets associated with the railroad, which may negatively affect theperformance of the PTC system.

SUMMARY

According to an embodiment, a method includes receiving first LightDetection and Ranging (LiDAR) data associated with a railroadenvironment, extracting an asset from the first LiDAR data associatedwith the railroad environment, and superimposing the asset into aspatial model. The method also includes receiving a field indicationassociated with a modification to the railroad environment and modifyingthe spatial model in response to receiving the field indicationassociated with the modification to the railroad environment. The methodfurther includes receiving second LiDAR data associated with therailroad environment and comparing the second LiDAR data to the modifiedspatial model.

According to another embodiment, a system includes one or moreprocessors and a memory storing instructions that, when executed by theone or more processors, cause the one or more processors to performoperations including receiving first LiDAR data associated with arailroad environment, extracting an asset from the first LiDAR dataassociated with the railroad environment, and superimposing the assetinto a spatial model. The operations also include receiving a fieldindication associated with a modification to the railroad environmentand modifying the spatial model in response to receiving the fieldindication associated with the modification to the railroad environment.The operations further include receiving second LiDAR data associatedwith the railroad environment and comparing the second LiDAR data to themodified spatial model.

According to yet another embodiment, one or more computer-readablestorage media embody instructions that, when executed by a processor,cause the processor to perform operations including receiving firstLiDAR data associated with a railroad environment, extracting an assetfrom the first LiDAR data associated with the railroad environment, andsuperimposing the asset into a spatial model. The operations alsoinclude receiving a field indication associated with a modification tothe railroad environment and modifying the spatial model in response toreceiving the field indication associated with the modification to therailroad environment. The operations further include receiving secondLiDAR data associated with the railroad environment and comparing thesecond LiDAR data to the modified spatial model.

Technical advantages of certain embodiments of this disclosure mayinclude one or more of the following. Certain systems and methodsdescribed herein identify and validate PTC critical assets withoutmanual measurements on or near the railroad, which improves the safetyand efficiency of identifying and validating assets. Certain systems andmethods described herein leverage LiDAR to identify and validate PTCcritical assets, which improves the accuracy of identifying andvalidating assets.

Other technical advantages will be readily apparent to one skilled inthe art from the following figures, descriptions, and claims. Moreover,while specific advantages have been enumerated above, variousembodiments may include all, some, or none of the enumerated advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

To assist in understanding the present disclosure, reference is now madeto the following description taken in conjunction with the accompanyingdrawings, in which:

FIG. 1 illustrates an example system for auditing assets;

FIG. 2 illustrates another example system for auditing assets;

FIG. 3 illustrates an example method for auditing assets;

FIG. 4 illustrates example output generated by an auditing module; and

FIG. 5 illustrates an example computer system that may be used by thesystems and methods described herein.

DETAILED DESCRIPTION

Certain embodiments of this disclosure include systems and methods forauditing assets by comparing data (e.g., LiDAR data and field data)captured at different times. The assets may be PTC critical assetsassociated with a railroad environment that are audited for PTCcompliance.

FIGS. 1 through 5 show example systems and methods for auditing assets.FIG. 1 shows an example system for auditing assets and FIG. 2 showsanother example system for auditing assets. FIG. 3 shows an examplemethod for auditing assets. FIG. 4 shows example output generated by anauditing module. FIG. 5 shows an example computer system that may beused by the systems and methods described herein.

FIG. 1 illustrates an example system 100 for auditing assets. System 100of FIG. 1 includes a network 110, an auditing module 120, a LiDARvehicle 170, and an observer 180. System 100 or portions thereof may beassociated with an entity, which may include any entity, such as abusiness, company (e.g., a railway company, a transportation company,etc.), or a government agency (e.g., a department of transportation, adepartment of public safety, etc.) that may audit assets. The elementsof system 100 may be implemented using any suitable combination ofhardware, firmware, and software.

Network 110 of system 100 may be any type of network that facilitatescommunication between components of system 100. Network 110 may connectauditing module 120 to LiDAR vehicle 170 of system 100. Although thisdisclosure shows network 110 as being a particular kind of network, thisdisclosure contemplates any suitable network. One or more portions ofnetwork 110 may include an ad-hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), a portion of the Internet, a portion ofthe Public Switched Telephone Network (PSTN), a cellular telephonenetwork, a 3G network, a 4G network, a 5G network, a Long Term Evolution(LTE) cellular network, a combination of two or more of these, or othersuitable types of networks. One or more portions of network 110 mayinclude one or more access (e.g., mobile access), core, and/or edgenetworks. Network 110 may be any communications network, such as aprivate network, a public network, a connection through Internet, amobile network, a WI-FI network, a Bluetooth network, etc. Network 110may include cloud computing capabilities. One or more components ofsystem 100 may communicate over network 110. For example, auditingmodule 120 may communicate over network 110, including receivinginformation from LiDAR vehicle 170.

Auditing module 120 of system 100 represents any suitable computingcomponent that may be used to audit assets 154. Auditing module 120 maybe communicatively coupled to LiDAR vehicle 170 via network 110.Auditing module 120 includes an interface 122, a memory 124, and aprocessor 126.

Interface 122 of auditing module 120 represents any suitable computerelement that can receive information from network 110, transmitinformation through network 110, perform suitable processing of theinformation, communicate to other components (e.g., LiDAR vehicle 170)of system 100 of FIG. 1, or any combination of the preceding. Interface122 represents any port or connection, real or virtual, including anysuitable combination of hardware, firmware, and software, includingprotocol conversion and data processing capabilities, to communicatethrough a LAN, a WAN, or other communication system that allows system100 of FIG. 1 to exchange information between components of system 100.

Memory 124 of auditing module 120 stores, permanently and/ortemporarily, received and transmitted information, as well as systemsoftware, control software, other software for auditing module 120, anda variety of other information. Memory 124 may store information forexecution by processor 126. Memory 124 includes any one or a combinationof volatile or non-volatile local or remote devices suitable for storinginformation. Memory 124 may include Random Access Memory (RAM),Read-only Memory (ROM), magnetic storage devices, optical storagedevices, or any other suitable information storage device or acombination of these devices. Memory 124 may include any suitableinformation for use in the operation of auditing module 120.Additionally, memory 124 may be a component external to (or may bepartially external to) auditing module 120. Memory 124 may be located atany location suitable for memory 124 to communicate with auditing module120. In the illustrated embodiment of FIG. 1, memory 124 of auditingmodule 120 stores a data collection engine 130, a model modificationengine 132, a comparison engine 134, a reporting engine 136, and adatabase 150. In certain embodiments, data collection engine 130, modelmodification engine 132, comparison engine 134, reporting engine 136,and/or database 150 may be external to memory 124 and/or auditing module120.

Data collection engine 130 of auditing module 120 is an application thatcollects data from one or more components of system 100. Data collectionengine 130 may collect data from LiDAR vehicle 170. For example, datacollection engine 130 may collect LiDAR data 152 (e.g., digital images)and/or GPS data from one or more components of LiDAR vehicle 170 vianetwork 110. Data collection engine 130 may collect data from observer180. For example, data collection engine 130 may collect one or morefield indications 158 from a user device (e.g., a smartphone, a tablet,a laptop computer, etc.) associated with observer 180.

Data collection engine 130 may utilize one or more programs to generatea spatial model 156. For example, data collection engine 130 may use ageographic information system (GIS) and/or LiDAR visualization softwareto generate spatial model 156. GIS integrates different types of data.For example, GIS may analyze spatial locations and organize layers ofinformation into spatial model 156 using maps, two dimensional (2D)scenes, and/or three dimensional (3D) scenes. The 2D scenes may includeorthographic imagery generated from LiDAR point cloud data. LiDARvisualization software may be used by data collection engine 130 to readand interpret LiDAR data 152. Data collection engine 130 may generatespatial model 156 using LiDAR data 152, GPS data, one or more fieldindications 158, one or more images (e.g., a LiDAR image), one or morepoint clouds, any other suitable data, or any suitable combination ofthe preceding. Data collection engine 130 may extract one or more assets154 from LiDAR data 152. Data collection engine 130 may superimposeasset 154 into spatial model 156.

Data collection engine 130 may use machine learning to intelligently andautomatically identify assets 154. In certain embodiments, datacollection engine 130 may use machine learning to extract assets 154from LiDAR data 152. One or more machine learning algorithms mayidentify assets 154 and compare assets 154 to a database to audit thepresence, location, and/or other characteristics of assets 154 withinthe environment captured by LiDAR data 152.

Model modification engine 132 of auditing module 120 is an applicationthat modifies spatial model 156. Model modification engine 132 maymodify spatial model 156 in response to one or more conditions. Forexample, model modification engine 132 may model spatial model 156 inresponse to receiving field indication 158 that an environment capturedby LiDAR data 152 will be or has been modified. Field indication 158 mayrepresent that asset 154 will be or has been physically moved from afirst location to a second location within the environment captured byLiDAR data 152. Field indication 158 may represent that asset 154 willbe or has been physically removed from the environment captured by LiDARdata 152. Field indication 158 may represent that asset 154 will be orhas been added to the environment captured by LiDAR data 152.

Comparison engine 134 of auditing module 120 is an application thatcompares data. For example, spatial models 156 may include spatialmodels 156 a-n (where n represents any suitable integer), and comparisonengine 134 may compare data within first spatial model 156 a generatedat time T1 to data within second spatial model 156 b generated at timeT2, where time T2 is any time after time T1. Comparison engine 134 maydetermine, based on the comparison of two or more spatial models 156,whether an anomaly exists between two or more spatial models 156. Forexample, comparison engine 134 may determine that a location of asset154 within first spatial model 156 a is different than a location ofasset 154 within second spatial model 156 b. As another example,comparison engine 134 may determine that asset 154 within first spatialmodel 156 a is not present within second spatial model 156 b.

Comparison engine 134 may verify, based on the comparison of two or morespatial models 156, that the information within the compared two or morespatial models 156 is the same. For example, comparison engine 134 mayconfirm that the location of asset 154 within first spatial model 156 amatches the location of asset 154 within second spatial model 156 b.Confirmation by comparison engine 134 that the location of asset 154within first spatial model 156 a matches the location of asset 154within second spatial model 156 b may be based on a predeterminedtolerance. For example, comparison engine 134 may confirm that thelocation within first spatial model 156 a matches the location of asset154 within second spatial model 156 b in the event the locations aredetermined to be within 2.2 meters of each other.

Reporting engine 136 of auditing module 120 is an application thatgenerates one or more reports 160. Reporting engine 136 may generatereport 160 in response to comparison engine 134 making one or moredeterminations. For example, reporting engine 136 may generate report160 in response to comparison engine 134 determining that an anomalyexists between two or more spatial models 156. As another example,reporting engine 136 may generate report 160 in response to comparisonengine 134 determining that the information between two or more datasets (e.g., two or more spatial models 156) is the same.

Database 150 of auditing module 120 may store certain types ofinformation for auditing module 120. For example, database 150 may storeLiDAR data 152, one or more assets 154, one or more spatial models 156,one or more field indications 158, and one or more reports 160. LiDARdata 152 is any data generated using LiDAR. LiDAR data 152 may includeone or more digital images. In certain embodiments, a digital image ofthe LiDAR data 152 may be a 360 degree image that has a range ofapproximately 600 feet each side of a centerline of a railroad trackwithin the railroad environment. In the illustrated embodiment of FIG.1, LiDAR data 152 is communicated from LiDAR vehicle 170 to auditingmodule 120 of system 100 via network 110.

Assets 154 are data extracted from LiDAR data 152 that representphysical objects in an environment. For example, assets 154 may beimages extracted from LiDAR data 152 that represent physical objectswithin a railroad environment. In certain embodiments, assets 154 may bePTC critical assets. PTC is a system of functional requirements formonitoring and controlling train movements. Each asset 154 may representone or more of the following physical objects within the railroadenvironment: a train-controlled signal (e.g., a signal governing trainmovement), a switch point, a crossing at grade, a mile post sign, aspeed sign, a clearance point, and the like.

Spatial models 156 are 2D and 3D models that represent one or moreenvironments. Each spatial model 156 may include vector data and/orraster data. Vector data of spatial model 156 may represent one or moreassets 154 as discrete points, lines, and/or polygons. Raster data ofspatial model 156 may represent one or more assets 154 as a rectangularmatrix of square cells. Spatial models 156 may be stored in a GISdatabase. One or more spatial models 156 may include LiDAR vector dataand/or LiDAR raster data. One or more spatial models 156 may includeLiDAR point cloud data. The LiDAR point cloud data may be converted to avector and/or raster format. One or more spatial models 156 may includeone or more assets 154. Each asset 154 has a location within spatialmodel 156. Each asset 154 within spatial model 156 may include one ormore attributes. Asset attributes specify a characteristic (e.g., aquality, aspect, version, etc.) that can be applied to asset 154.

Field indications 158 are indications of changes to physical objectswithin an environment. Field indications 158 may include indications ofanticipatory changes to physical objects within the environment. Fieldindication 158 may indicate that asset 154 will change location or haschanged location within an environment captured by LiDAR data 152. Forexample, field indication 158 may indicate that a speed sign isscheduled to move 20 feet within a railroad environment. As anotherexample, field indication 158 may indicate that a speed sign has moved20 feet within a railroad environment. Field indication 158 may indicatethat asset 154 will be or has been removed from the environment capturedby LiDAR data 152. For example, field indication 158 may indicate that acrossing at grade is scheduled to be removed from the railroadenvironment. As another example, field indication 158 may indicate thata crossing at grade has been removed from the railroad environment.Field indication 158 may indicate that asset 154 will be or has beenadded to the environment captured by LiDAR data 152. For example, fieldindication 158 may indicate that a mile post sign is scheduled to beadded to a railroad environment. As another example, field indication158 may indicate that a mile post sign has been added to a railroadenvironment.

Reports 160 are communications generated in response to determinationsmade by auditing module 120 (e.g., comparison engine 134). One or morereports 160 may be verbal and/or written communications. One or morereports 160 may be generated electronically by a machine and/orphysically by a human being. Report 160 may include informationindicating an anomaly exists between two or more spatial models 156.Report 160 may include information verifying that the informationbetween two or more spatial models 156 is the same. Report 160 mayinclude lists, charts, tables, diagrams, and the like. For example,report 160 may include table 410 of FIG. 4, which illustrates exampleauditing results generated by auditing module 120.

Database 150 may be any one or a combination of volatile or non-volatilelocal or remote devices suitable for storing information. Database 150may include RAM, ROM, magnetic storage devices, optical storage devices,or any other suitable information storage device or a combination ofthese devices. Database 150 may be a component external to auditingmodule 120. Database 150 may be located in any location suitable fordatabase 150 to store information for auditing module 120. For example,database 150 may be located in a cloud environment.

Processor 126 of auditing module 120 controls certain operations ofauditing module 120 by processing information received from interface122 and memory 124 or otherwise accessed by processor 126. Processor 126communicatively couples to interface 122 and memory 124. Processor 126may include any hardware and/or software that operates to control andprocess information. Processor 126 may be a programmable logic device, amicrocontroller, a microprocessor, any suitable processing device, orany suitable combination of the preceding. Additionally, processor 126may be a component external to auditing module 120. Processor 126 may belocated in any location suitable for processor 126 to communicate withauditing module 120. Processor 126 of auditing module 120 controls theoperations of data collection engine 130, model modification engine 132,comparison engine 134, and reporting engine 136.

LiDAR vehicle 170 of system 100 represents a vehicle (e.g., a van, atruck, a car, a rail car, etc.) that collects LiDAR data 152 (e.g.,digital images). LiDAR vehicle 170 may include one or more scanningand/or imaging sensors. The sensors may create one or more images (e.g.,a 3D point cloud) that facilitate auditing module 120 in detectingassets 154. LiDAR vehicle 170 may collect GPS data. The LiDAR and GPSdata may be used to generate a 360-degree real world view of a railroadenvironment. In certain embodiments, LiDAR vehicle communicates data(e.g., LiDAR data 152 and/or GPS data) to auditing module 120.

Observer 180 of system 100 is any human or machine that observes theenvironment captured by LiDAR data 152. Observer 180 may be an inspector(e.g., a railroad inspector), an engineer (e.g., a rail field engineeror a safety engineer), a passer-by (e.g., a pedestrian, a driver, etc.),a law enforcement agent (e.g., a police officer), a camera (e.g., avideo camera), and the like. Observer 180 may communicate information(e.g., field indication 158) to auditing module 120 via a webapplication (e.g., a work order application), a phone call, a textmessage, an email, a report, and the like. Observer 180 may communicateinformation to auditing module 120 using a phone (e.g., a smartphone), atablet, a laptop computer, or any other suitable device.

Although FIG. 1 illustrates a particular arrangement of network 110,auditing module 120, interface 122, memory 124, processor 126, datacollection engine 130, model modification engine 132, comparison engine134, reporting engine 136, database 150, LiDAR data 152, assets 154,spatial models 156, field indications 158, reports 160, LiDAR vehicle170, and observer 180, this disclosure contemplates any suitablearrangement of network 110, auditing module 120, interface 122, memory124, processor 126, data collection engine 130, model modificationengine 132, comparison engine 134, reporting engine 136, database 150,LiDAR data 152, assets 154, spatial models 156, field indications 158,reports 160, LiDAR vehicle 170, and observer 180. Network 110, auditingmodule 120, interface 122, memory 124, processor 126, data collectionengine 130, model modification engine 132, comparison engine 134,reporting engine 136, database 150, and LiDAR vehicle 170 may bephysically or logically co-located with each other in whole or in part.

Although FIG. 1 illustrates a particular number of networks 110,auditing modules 120, interfaces 122, memories 124, processors 126, datacollection engines 130, model modification engine 132, comparison engine134, reporting engine 136, database 150, LiDAR data 152, assets 154,spatial models 156, field indications 158, reports 160, LiDAR vehicles170, and observers 180, this disclosure contemplates any suitable numberof networks 110, auditing modules 120, interfaces 122, memories 124,processors 126, data collection engines 130, model modification engine132, comparison engine 134, reporting engine 136, database 150, LiDARdata 152, assets 154, spatial models 156, field indications 158, reports160, LiDAR vehicles 170, and observers 180. One or more components ofauditing module 120 and/or LiDAR vehicle 170 may be implemented usingone or more components of the computer system of FIG. 5.

Although FIG. 1 describes system 100 for auditing assets 154, one ormore components of system 100 may be applied to other implementations.For example, one or more components of auditing module 120 may beutilized for asset identification and/or inventory.

In operation, data collection engine 130 of auditing module 120 ofsystem 100 receives LiDAR data 152 from LiDAR vehicle 170 at time T1 vianetwork 110. LiDAR data 152 is associated with a railroad environment.Data collection engine 130 extracts asset 154 from LiDAR data 152associated with the railroad environment and superimposes asset 154 onspatial model 156. Data collection engine 130 receives field indication158 at time T2 from observer 180 (e.g., a rail field engineer) that therailroad environment will be or has been modified. Model modificationengine 132 modifies spatial model 156 in response to receiving fieldindication 158 that the railroad environment will be or has beenmodified. Data collection engine 130 then receives LiDAR data 152 attime T3. LiDAR data 152 received at time T3 is associated with therailroad environment. Comparison engine 134 compares LiDAR data 152received at time T3 to modified spatial model 156. Comparison engine 134determines that the location of asset 154 within modified spatial model146 is the same as the location of asset 154 within LiDAR data 152received at time T3. Reporting engine 136 generates a report verifyingthat the location of asset 154 in modified spatial model 146 isaccurate.

As such, system 100 of FIG. 1 validates assets 154 in spatial model 156without manual measurements on or near the railroad, which improves thesafety and efficiency of validating assets.

FIG. 2 illustrates an example system 200 for auditing assets that usesauditing module 120 of FIG. 1. System 200 of FIG. 2 includes a railroadenvironment 210 and auditing module 120. Railroad environment 210 is anarea encompassing one or more railroad tracks 220. Railroad environment210 may be associated with a division and/or a subdivision. The divisionis the portion of the railroad under the supervision of asuperintendent. The subdivision is a smaller portion of the division.The subdivision may be a crew district and/or a branch line. In theillustrated embodiment of FIG. 2, railroad environment 210 includesphysical object 230. Physical object 230 may be any tangible componentwithin railroad environment 210, such as a train-controlled signal, aswitch point, a crossing at grade, a mile post sign, a speed sign, aclearance point, and the like. In the illustrated embodiment of FIG. 2,physical object 230 is a sign.

System 200 of FIG. 2 includes an illustration of railroad environment210 at three moments in time: T1, T2, and T3. At time T1 in theillustrated embodiment of FIG. 2, railroad environment 210 includesLiDAR vehicle 170. LiDAR vehicle 170 captures LiDAR data 152 a at timeT1. Time T1 represents the period of time required for LiDAR vehicle 170to capture LiDAR data 152 a. Time T1 may depend on the travel speed ofLiDAR vehicle 170 as LiDAR vehicle 170 captures LiDAR data 152 a. LiDARdata 152 a indicates that physical object 230 is at physical objectlocation X at time T1. In certain embodiments, LiDAR vehicle 170communicates LiDAR data 152 a to auditing module 120 of system 200.

Auditing module 120 of system 200 extracts asset 154 from LiDAR data 152a. Asset 154 represents physical object 230 of railroad environment 210at time T1. Auditing module 120 then superimposes asset 154 on spatialmodel 156 a such that spatial model 156 a represents railroadenvironment 210 at time T1. In certain embodiments, auditing module 120generates a railroad track centerline 222 in spatial model 156 a.Auditing module 120 may extract one or more assets corresponding torailroad track 220 from LiDAR data 152 a, superimpose the one or moreassets corresponding to railroad track 220 on spatial model 156 a,determine railroad track centerline 222 based on the assetscorresponding to railroad track 220, and generate railroad trackcenterline 222 for spatial model 156 a. In certain embodiments, railroadtrack centerline 222 is a line that is centered between the two outerrails of railroad track 220. Auditing module 120 may use railroad trackcenterline 222 as a reference line for the location of asset 154. Inspatial model 156 a, auditing module 120 translates the location ofasset 154 from its actual location to a corresponding location alongrailroad track centerline 222, as shown by asset location X.

At time T2 in the illustrated embodiment of FIG. 2, railroad environment210 includes observer 180. Observer 180 notices a modification inrailroad environment 210 at time T2. Time T2 represents the period oftime required for observer 180 to capture the modification in railroadenvironment 210. The modification may be a relocation of physical object230 within railroad environment 210, the removal of physical object 230from railroad environment 210, an addition of a second physical object230 to railroad environment 210, and the like. Observer 180 maycommunicate field indication 158 that railroad environment 180 will beor has been modified to auditing module 120. In the illustratedembodiment of FIG. 2, field indication 158 represents an observation byobserver 180 that physical object 230 has been physically moved fromphysical object location X to physical object location Y.

Auditing module 120 of system 200 receives field indication 158 fromobserver 180. For example, auditing module 120 of system 200 may receivefield indication 158 from observer 180 via a web application (e.g., awork order application), email, phone call, text message, fax, report,etc. In response to receiving field indication 158, auditing module 120modifies spatial model 156 a to generate spatial model 156 b. Forexample, a user (e.g., an administrator) may edit spatial model 156 a tomove asset 154 from asset location X to asset location Y, as illustratedin spatial model 156 b. In spatial model 156 b, auditing module 120translates the location of asset 154 from its actual location to acorresponding location along railroad track centerline 222, as shown byasset location Y.

At time T3 in the illustrated embodiment of FIG. 2, LiDAR vehicle 170captures LiDAR data 152 b. Time T3 represents the period of timerequired for LiDAR vehicle 170 to capture LiDAR data 152 b. Time T3 maydepend on the travel speed of LiDAR vehicle 170 as LiDAR vehicle 170captures LiDAR data 152 b. LiDAR data 152 b indicates that physicalobject 230 is at physical object location Y at time T3. In certainembodiments, LiDAR vehicle 170 communicates LiDAR data 152 b to auditingmodule 120 of system 200.

Auditing module 120 of system 200 extracts asset 154 from LiDAR data 152b. Asset 154 represents physical object 230 of railroad environment 210at time T3. Auditing module 120 then superimposes asset 154 on spatialmodel 156 c such that spatial model 156 c represents railroadenvironment 210 at time T3. In spatial model 156 c, auditing module 120translates the actual location of asset 154 to a corresponding locationalong railroad track centerline 222, as shown by asset location Y.Auditing module 120 may then compare spatial model 156 c to spatialmodel 156 b to verify that the changes to railroad environment 210 havebeen accurately captured.

Although FIG. 2 illustrates a particular arrangement of the componentsof system 200, this disclosure contemplates any suitable arrangement ofthe components of system 200. Although FIG. 2 illustrates a particularnumber of auditing modules 120, spatial models 156 a, 156 b, and 156 c,assets 154, asset locations X and Y, LiDAR vehicles 170, time periodsT1, T2, and T3, physical objects 230, physical object locations X and Y,railroad tracks 220, and railroad track centerlines 222, this disclosurecontemplates any suitable number of auditing modules 120, spatial models156 a, 156 b, and 156 c, assets 154, asset locations X and Y, LiDARvehicles 170, time periods T1, T2, and T3, physical objects 230,physical object locations X and Y, railroad tracks 220, and railroadtrack centerlines 222. For example, system 200 of FIG. 2 may includemultiple physical objects 230 a-n and multiple assets 154 a-n, where nrepresents any suitable integer.

FIG. 3 shows an example method 300 for auditing assets. Method 300begins at step 305. At step 310, an auditing module (e.g., auditingmodule 120 of FIG. 2) receives first LiDAR data (e.g., LiDAR data 152 aof FIG. 2) associated with a railroad environment (e.g., railroadenvironment 210 of FIG. 2) from a LiDAR vehicle (e.g., LiDAR vehicle 170of FIG. 2). The railroad environment includes a railroad track (e.g.,railroad track 220 of FIG. 2). Method 300 then moves from step 310 tostep 320. At step 320, the auditing module extracts an asset (e.g., asst154 of FIG. 2) from the LiDAR data. The asset may represent a physicalobject (e.g., physical object 230 of FIG. 2), such as a train-controlledsignal, a switch point, a crossing at grade, a mile post sign, a speedsign, a clearance point, etc. Method 300 then moves from step 320 tostep 330.

At step 330, the auditing module superimposes the asset into a spatialmodel (e.g., spatial model 156 a of FIG. 2). The location of the assetin the spatial model may be translated from its actual location to alocation along a centerline of the railroad track in the railroadenvironment (e.g., railroad track centerline 222 of FIG. 2). Method 300then moves from step 330 to step 340.

At step 340, the auditing module determines whether a field indication(e.g., field indication 158 of FIG. 2) has been received that indicatesthat the railroad environment will be or has been modified. For example,the auditing module may receive a field indication indicating that thephysical object will be or has been moved within the railroadenvironment captured by the first LiDAR data, that the physical objectwill be or has been removed from the railroad environment captured bythe first LiDAR data, or that a new physical object will be or has beenadded to the railroad environment captured by the first LiDAR data.

If the auditing module determines that a field indication has not beenreceived that indicates that the railroad environment will be or hasbeen modified, method 300 advances from step 340 to step 360. If theauditing module determines that a field indication has been receivedthat indicates that the railroad environment will be or has beenmodified, method 300 moves from step 340 to step 350.

At step 350, the auditing module modifies the spatial model inaccordance with the field indication. For example, the auditing modulemay move the location of the asset from location X along the centerlineof the railroad track to location Y along the centerline of the railroadtrack. As another example, the auditing module may remove the asset fromthe spatial model. As still another example, the auditing module may addan asset to the spatial model. Modifying the spatial model results in amodified spatial model (e.g., spatial model 156 b of FIG. 2.) Method 300then moves from step 350 to step 360.

At step 360, the auditing module receives second LiDAR data (e.g., LiDARdata 152 b of FIG. 2) associated with the railroad environment. Thesecond LiDAR data is captured at a later time than the first LiDAR data.For example, the second LiDAR data may be captured by the LiDAR vehiclea month, a year, or five years after the first LiDAR data is captured bythe LiDAR vehicle. Method 300 then moves from step 360 to step 370. Atstep 370, the auditing module compares the second LiDAR data to thespatial model. For example, the auditing module may compare a locationof asset 154 in the modified spatial model to a location of asset 154 asindicated by the second LiDAR data. Method 300 then moves from step 370to step 380.

At step 380, the auditing module determines whether an anomaly existsbetween the second LiDAR data and the spatial model. For example, theauditing module may determine that the location of the asset in themodified spatial model is different than the location of the asset inthe second LiDAR data. If the auditing module determines that an anomalyexists between the second LiDAR data and the spatial model, method 300moves from step 380 to step 385, where the auditing module generates areport indicating the anomaly. If the auditing module determines that ananomaly does not exist between the second LiDAR data and the spatialmodel, method 300 advances from step 380 to step 390, where the auditingmodule validates the asset data in the spatial model. Method 300 thenmoves to steps 385 and 390 to step 395, where method 300 ends.

Modifications, additions, or omissions may be made to method 300depicted in FIG. 3. Method 300 may include more, fewer, or other steps.For example, method 300 may include generating a report in response tovalidating the asset data at step 390. Steps may be performed inparallel or in any suitable order. While discussed as specificcomponents completing the steps of method 300, any suitable componentmay perform any step of method 300. For example, the auditing module mayreceive the first LiDAR data from a first LiDAR vehicle and receive thesecond LiDAR data from a second LiDAR vehicle that is different than thefirst LiDAR vehicle.

FIG. 4 illustrates example output 400 that may be generated by auditingmodule 120 of FIG. 1. Output 400 of FIG. 4 may be in any form, such as atable, a chart, a list, and the like. In the illustrated embodiment ofFIG. 4, output 400 is a chart 410. Chart 410 shows audit results 420from auditing assets 154 using auditing module 120 of FIG. 1. Theauditing process involves comparing assets 154 determined from LiDARdata to assets 154 determined at an earlier time. For example, theauditing process may involve comparing assets 154 within spatial model156 c of FIG. 2, which incorporates LiDAR data 152 b, to assets 154within spatial model 156 b of FIG. 2, which incorporates LiDAR data 152a and one or more field indications 158.

Chart 410 includes ten columns labeled 430 through 448. Column 430 showsassets 154 analyzed by auditing module 120 of FIG. 1. Assets 154represent the following physical objects associated with a railroadenvironment: clearance point, crossing at grade, milepost sign, signal,speed sign, and switch. Column 432 of table 410 shows the total numberof assets 154 analyzed by auditing module 120 of FIG. 1. In theillustrated embodiment of FIG. 4, auditing module 120 analyzed 117clearance points, 269 crossings, 27 milepost signs, 199 signals, 98speed signs, and 84 switches, which totals to 974 total assets 154.

Column 434 of table 410 shows passes determined by auditing module 120of FIG. 1. Passes represent matches between the data within apredetermined tolerance. For example, out of the 117 clearance pointsanalyzed in a first model (e.g., spatial model 156 c of FIG. 2), thelocations of 117 clearance points matched the locations of 117 clearancepoints in the second model (e.g., spatial model 156 b of FIG. 2) withina predetermined tolerance (e.g., 2.2 meters). Column 436 of table 410shows exceptions determined by auditing module 120 of FIG. 1. Exceptionsrepresent anomalies between the data. For example, out of the 207milepost signs analyzed in a first model (e.g., spatial model 156 c ofFIG. 2), 4 milepost signs were either missing or in a different locationin the second model (e.g., spatial model 156 b of FIG. 2).

Column 438 of table 410 shows the percentage of assets 154 that passed,which is the pass number of assets 154 shown in column 434 divided bythe total number of assets 154 shown in column 432, represented as apercentage. Column 440 shows exceptions found with no change managementprocess (CMP). CMP represents a change that has been indicated toauditing module 120 (e.g., a field indication). Column 442 showsexceptions found with an incorrect CMP. An incorrect CMP represents aCMP that has been implemented incorrectly in the spatial model. Forexample, the incorrect CMP may represent a milepost sign that wasrelocated in the spatial model twenty feet, but the CMP indicated thatthe milepost sign should be relocated in the spatial model ten feet.

Column 444 of table 410 shows CMP found but no geographic informationsystem (GIS) edit, which represents a change that has been indicated toauditing module 120 (e.g., a field indication) but has not yet beenimplemented in the spatial model. Column 446 of table 410 showsexceptions found due to office error, and column 448 of table 410 showsassets 154 that could not be verified.

Modifications, additions, or omissions may be made to output 400depicted in FIG. 4. For example, output 400 may be represented in aformat other than chart 410 such as a list or a graph. Table 410 mayinclude more, fewer, or other columns and/or rows. For example, table410 may include more or less than ten columns and 6 assets. In certainembodiments, output 400 may be included in one or more reports (e.g.,report 160 of FIG. 1).

FIG. 5 shows an example computer system that may be used by the systemsand methods described herein. For example, network 110, auditing module120, and LiDAR vehicle 170 of FIG. 1 may include one or moreinterface(s) 510, processing circuitry 520, memory(ies) 530, and/orother suitable element(s). Interface 510 (e.g., interface 122 of FIG. 1)receives input, sends output, processes the input and/or output, and/orperforms other suitable operation. Interface 510 may comprise hardwareand/or software.

Processing circuitry 520 (e.g., processor 126 of FIG. 1) performs ormanages the operations of the component. Processing circuitry 520 mayinclude hardware and/or software. Examples of a processing circuitryinclude one or more computers, one or more microprocessors, one or moreapplications, etc. In certain embodiments, processing circuitry 520executes logic (e.g., instructions) to perform actions (e.g.,operations), such as generating output from input. The logic executed byprocessing circuitry 520 may be encoded in one or more tangible,non-transitory computer readable media (such as memory 530). Forexample, the logic may comprise a computer program, software, computerexecutable instructions, and/or instructions capable of being executedby a computer. In particular embodiments, the operations of theembodiments may be performed by one or more computer readable mediastoring, embodied with, and/or encoded with a computer program and/orhaving a stored and/or an encoded computer program.

Memory 530 (or memory unit) stores information. Memory 530 (e.g., memory124 of FIG. 1) may comprise one or more non-transitory, tangible,computer-readable, and/or computer-executable storage media. Examples ofmemory 530 include computer memory (for example, RAM or ROM), massstorage media (for example, a hard disk), removable storage media (forexample, a Compact Disk (CD) or a Digital Video Disk (DVD)), databaseand/or network storage (for example, a server), and/or othercomputer-readable medium.

Herein, a computer-readable non-transitory storage medium or media mayinclude one or more semiconductor-based or other integrated circuits(ICs) (such as field-programmable gate arrays (FPGAs) orapplication-specific ICs (ASICs)), hard disk drives (HDDs), hybrid harddrives (HHDs), optical discs, optical disc drives (ODDs),magneto-optical discs, magneto-optical drives, floppy diskettes, floppydisk drives (FDDs), magnetic tapes, solid-state drives (SSDs),RAM-drives, SECURE DIGITAL cards or drives, any other suitablecomputer-readable non-transitory storage media, or any suitablecombination of two or more of these, where appropriate. Acomputer-readable non-transitory storage medium may be volatile,non-volatile, or a combination of volatile and non-volatile, whereappropriate.

Herein, “or” is inclusive and not exclusive, unless expressly indicatedotherwise or indicated otherwise by context. Therefore, herein, “A or B”means “A, B, or both,” unless expressly indicated otherwise or indicatedotherwise by context. Moreover, “and” is both joint and several, unlessexpressly indicated otherwise or indicated otherwise by context.Therefore, herein, “A and B” means “A and B, jointly or severally,”unless expressly indicated otherwise or indicated otherwise by context.

The scope of this disclosure encompasses all changes, substitutions,variations, alterations, and modifications to the example embodimentsdescribed or illustrated herein that a person having ordinary skill inthe art would comprehend. The scope of this disclosure is not limited tothe example embodiments described or illustrated herein. Moreover,although this disclosure describes and illustrates respectiveembodiments herein as including particular components, elements,feature, functions, operations, or steps, any of these embodiments mayinclude any combination or permutation of any of the components,elements, features, functions, operations, or steps described orillustrated anywhere herein that a person having ordinary skill in theart would comprehend. Furthermore, reference in the appended claims toan apparatus or system or a component of an apparatus or system beingadapted to, arranged to, capable of, configured to, enabled to, operableto, or operative to perform a particular function encompasses thatapparatus, system, component, whether or not it or that particularfunction is activated, turned on, or unlocked, as long as thatapparatus, system, or component is so adapted, arranged, capable,configured, enabled, operable, or operative. Additionally, although thisdisclosure describes or illustrates particular embodiments as providingparticular advantages, particular embodiments may provide none, some, orall of these advantages.

What is claimed is:
 1. A method, comprising: receiving first LightDetection and Ranging (LiDAR) data associated with a railroadenvironment; extracting an asset from the first LiDAR data associatedwith the railroad environment; superimposing the asset into a spatialmodel; receiving a field indication associated with a modification tothe railroad environment; modifying the spatial model in response toreceiving the field indication associated with the modification to therailroad environment; receiving second LiDAR data associated with therailroad environment; and comparing the second LiDAR data to themodified spatial model.
 2. The method of claim 1, further comprisinggenerating a report in response to comparing the second LiDAR data tothe modified spatial model, wherein the report comprises one or more ofthe following: an anomaly between a location of the asset determinedfrom the modified spatial model and a location of the asset determinedfrom the second LiDAR data; and a confirmation that the location of theasset determined from the modified spatial model matches the location ofthe asset determined from the second LiDAR data.
 3. The method of claim1, wherein the field indication associated with the modification to therailroad environment represents one of the following: a physical objectrepresentative of the asset will be or has been physically moved from afirst location to a second location within the railroad environment; thephysical object representative of the asset will be or has beenphysically removed from the railroad environment; and a new physicalobject will be or has been added to the railroad environment.
 4. Themethod of claim 1, wherein: the first LiDAR data and the second LiDARdata are captured using a LiDAR vehicle, the LiDAR vehicle comprisingone or more scanning and imaging sensors; and the first LiDAR datarepresents a first 360 degree image; the second LiDAR data represents asecond 360 degree image; the first 360 degree image and the second 360degree image each has a range that represents approximately 600 feeteach side of a centerline of a railroad track within the railroadenvironment.
 5. The method of claim 1, wherein: the asset is a positivetrain control (PTC) critical asset; and the asset represents one of thefollowing physical objects: a train-controlled signal; a switch point; acrossing at grade; a mile post sign; a speed sign; and a clearancepoint.
 6. The method of claim 1, wherein: the spatial model is stored ina Geographic Information Systems (GIS) database; the spatial modelcomprises one or more of the following: vector data; and raster data. 7.The method of claim 1, further comprising auditing, in response tocomparing the second LiDAR data to the modified spatial model, therailroad environment for PTC compliance.
 8. A system comprising one ormore processors and a memory storing instructions that, when executed bythe one or more processors, cause the one or more processors to performoperations comprising: receiving first Light Detection and Ranging(LiDAR) data associated with a railroad environment; extracting an assetfrom the first LiDAR data associated with the railroad environment;superimposing the asset into a spatial model; receiving a fieldindication associated with a modification to the railroad environment;modifying the spatial model in response to receiving the fieldindication associated with the modification to the railroad environment;receiving second LiDAR data associated with the railroad environment;and comparing the second LiDAR data to the modified spatial model. 9.The system of claim 8, the operations further comprising generating areport in response to comparing the second LiDAR data to the modifiedspatial model, wherein the report comprises one or more of thefollowing: an anomaly between a location of the asset determined fromthe modified spatial model and a location of the asset determined fromthe second LiDAR data; and a confirmation that the location of the assetdetermined from the modified spatial model matches the location of theasset determined from the second LiDAR data.
 10. The system of claim 8,wherein the field indication associated with the modification to therailroad environment represents one of the following: a physical objectrepresentative of the asset will be or has been physically moved from afirst location to a second location within the railroad environment; thephysical object representative of the asset will be or has beenphysically removed from the railroad environment; and a new physicalobject will be or has been added to the railroad environment.
 11. Thesystem of claim 8, wherein: the first LiDAR data and the second LiDARdata are captured using a LiDAR vehicle, the LiDAR vehicle comprisingone or more scanning and imaging sensors; and the first LiDAR datarepresents a first 360 degree image; the second LiDAR data represents asecond 360 degree image; the first 360 degree image and the second 360degree image each has a range that represents approximately 600 feeteach side of a centerline of a railroad track within the railroadenvironment.
 12. The system of claim 8, wherein: the asset is a positivetrain control (PTC) critical asset; and the asset represents one of thefollowing physical objects: a train-controlled signal; a switch point; acrossing at grade; a mile post sign; a speed sign; and a clearancepoint.
 13. The system of claim 8, wherein: the spatial model is storedin a Geographic Information Systems (GIS) database; the spatial modelcomprises one or more of the following: vector data; and raster data.14. The system of claim 8, the operations further comprising auditing,in response to comparing the second LiDAR data to the modified spatialmodel, the railroad environment for PTC compliance.
 15. One or morecomputer-readable storage media embodying instructions that, whenexecuted by a processor, cause the processor to perform operationscomprising: receiving first Light Detection and Ranging (LiDAR) dataassociated with a railroad environment; extracting an asset from thefirst LiDAR data associated with the railroad environment; superimposingthe asset into a spatial model; receiving a field indication associatedwith a modification to the railroad environment; modifying the spatialmodel in response to receiving the field indication associated with themodification to the railroad environment; receiving second LiDAR dataassociated with the railroad environment; and comparing the second LiDARdata to the modified spatial model.
 16. The one or morecomputer-readable storage media of claim 15, the operations furthercomprising generating a report in response to comparing the second LiDARdata to the modified spatial model, wherein the report comprises one ormore of the following: an anomaly between a location of the assetdetermined from the modified spatial model and a location of the assetdetermined from the second LiDAR data; and a confirmation that thelocation of the asset determined from the modified spatial model matchesthe location of the asset determined from the second LiDAR data.
 17. Theone or more computer-readable storage media of claim 15, wherein thefield indication associated with the modification to the railroadenvironment represents one of the following: a physical objectrepresentative of the asset will be or has been physically moved from afirst location to a second location within the railroad environment; thephysical object representative of the asset will be or has beenphysically removed from the railroad environment; and a new physicalobject will be or has been added to the railroad environment.
 18. Theone or more computer-readable storage media of claim 15, wherein: thefirst LiDAR data and the second LiDAR data are captured using a LiDARvehicle, the LiDAR vehicle comprising one or more scanning and imagingsensors; and the first LiDAR data represents a first 360 degree image;the second LiDAR data represents a second 360 degree image; the first360 degree image and the second 360 degree image each has a range thatrepresents approximately 600 feet each side of a centerline of arailroad track within the railroad environment.
 19. The one or morecomputer-readable storage media of claim 15, wherein: the asset is apositive train control (PTC) critical asset; and the asset representsone of the following physical objects: a train-controlled signal; aswitch point; a crossing at grade; a mile post sign; a speed sign; and aclearance point.
 20. The one or more computer-readable storage media ofclaim 15, wherein: the spatial model is stored in a GeographicInformation Systems (GIS) database; the spatial model comprises one ormore of the following: vector data; and raster data.